default
chainalign-backend / services/EdgeCaseDetectionService / default
Variable: default
default:
object
Defined in: services/EdgeCaseDetectionService.js:403
Type Declaration
batchDetectEdgeCases()
batchDetectEdgeCases: (
skus) =>Promise<any[]>
Batch detect edge cases for multiple SKUs
Parameters
skus
any[]
Array of SKU data objects
Returns
Promise<any[]>
Array of results with SKU ID and edge case flags
detectDataQualityIssues()
detectDataQualityIssues: (
params) =>boolean
Detect data quality issues
Issues include:
- High percentage of missing values (> 20%)
- High percentage of outliers (> 10%)
- Stale data (last update > 30 days ago)
Parameters
params
demandHistory
number[]
Historical demand (may contain nulls)
lastUpdated
Date
Timestamp of last data update
missingThreshold
number = 0.2
Threshold for missing values (default: 0.2)
outlierThreshold
number = 0.1
Threshold for outliers (default: 0.1)
stalenessThreshold
number = 30
Days threshold for staleness (default: 30)
Returns
boolean
True if data quality issues detected
detectDependencies()
detectDependencies: (
demandHistory,otherSkuDemands,correlationThreshold) =>boolean
Detect cross-SKU dependencies Requires demand history for multiple SKUs to calculate correlation
Parameters
demandHistory
number[]
Demand history for target SKU
otherSkuDemands
number[][] = []
Array of demand histories for other SKUs
correlationThreshold
number = 0.7
Correlation threshold (default: 0.7)
Returns
boolean
True if strong dependency detected
detectEdgeCases()
detectEdgeCases: (
params) =>Promise<string[]>
Detect all edge cases for a single SKU
Parameters
params
demandHistory
number[]
Historical demand values
lastUpdated
Date
Last update timestamp
otherSkuDemands
number[][] = []
Optional: demand histories for dependency detection
skuId
string
SKU identifier
Returns
Promise<string[]>
Array of edge case flags
detectEventSensitivity()
detectEventSensitivity: (
demandHistory,cvThreshold) =>boolean
Detect event sensitivity (high variability around known dates) Uses coefficient of variation and checks for spikes
Parameters
demandHistory
number[]
Historical demand values
cvThreshold
number = 0.8
CV threshold for event sensitivity (default: 0.8)
Returns
boolean
True if event sensitive
detectLifecycleComplexity()
detectLifecycleComplexity: (
demandHistory) =>boolean
Detect lifecycle complexity (lumpy patterns, false starts, extended tail)
Indicators:
- Multiple zero-to-nonzero transitions (lumpy)
- Early peak followed by decline then recovery (false start)
- Long tail of low values after peak (extended tail)
Parameters
demandHistory
number[]
Historical demand values
Returns
boolean
True if lifecycle complexity detected
detectStructuralBreak()
detectStructuralBreak: (
demandHistory,threshold) =>boolean
Detect structural breaks using CUSUM (Cumulative Sum) algorithm A structural break indicates a significant change in demand level or pattern
Parameters
demandHistory
number[]
Historical demand values
threshold
number = 3.5
Threshold for detecting break (default: 3.5)
Returns
boolean
True if structural break detected